@JamesHMcLaren that's just completely absolutely untrue, I'm sorry, but.. infosec.exchange/@codinghorror…

Jeff Atwood

2025-09-28 03:06:32

bad news "AI bubble doomers". I've found the LLMs to be incredibly useful and reduce the workload (and/or make people much, MUCH more effective at their jobs with the "centaur" model).

Is it overhyped? FUCK Yes. Salespeople Gotta Always Be Closing. But this is NOTHING like the moronic Segway (I am still bitter about that crap), Cryptocurrency, which is all grifters and gamblers and criminals end-to-end, and the first dot-com bubble where not NEARLY enough people had broadband or even internet access, plus the logistics systems to support shipping products was nowhere REMOTELY where it is today.

If you are expecting this "AI bubble" to pop anytime soon, uh.. you might be waiting a bit longer than you think? Overhyped, yes, overbuilding, sure, but not remotely a true bubble any any of the same senses of the three examples I listed above 👆. There's something very real, very practical, very useful here, and it is getting better every day.

If you find this uncomfortable, I'm sorry, but I know what I know, and I can cite several dozen very specific examples in the last 2-3 weeks where it saved me, or my team, quite a bit of time.

@codinghorror @mikemccaffrey @JamesHMcLaren It’s the overhyping that’s the problem; when the hype bubble bursts, it’s not just going to take down the no-value “solutions”, it’s also going to crater the actually-valuable propositions because those products aren’t profitable without the hype money coming in.

(My wife was a pre-IPO employee of Amazon; I watched the dot-com collapse in extreme close-up.)

@doragasu

It's not the data centers. It's the overvaluation of "AI" stocks that pushes bubble money into pension funds

When that bubble valuation goes away, the idled data centers will be there like the feet of Ozymandias, and the pension funds and 401(k)s will be the barren desert all around

@nixCraft
@cstross

@codinghorror
Some of it is useful, but note the point in the article: it is not adding anything to the GDP in terms of productivity. I’ve seen people use LLMs as slightly better interfaces to documentation. Yes, it’s useful, but it isn’t a killer app. People use the, for summarisation but they’re really bad at it and they often miss the key point. Using that in decision making lets you move faster, but in the wrong direction. It takes a while for that to appear and then it’s a problem. People are ‘vibe coding’ business apps, but the ones that work are the kind that you could build in less time with MS PowerApps and no programming ability beyond Excel formulae. Producing moderately bad subtitles is better than having no subtitles. Low-stakes translation was already pretty good pre-LLM and is now better (but still makes ludicrous mistakes and so can’t be trusted in situations where being misunderstood costs money or lives).

And all of these use cases are massively subsidised. How many people are willing to pay ten times OpenAI’s current price for LLMs? Inference costs dropping relies on the ML-tailored GPUs having large economies of scale, which relies on large numbers of consumers. Increase the price and that cycle ends. Training costs are increasing and must be amortised over more users, increase the costs and this stops, so you must increase the costs more.

I’ve seen estimates that, without VC money being set on fire, a ChatGPT subscription would cost at least $1,000/month. For a thing that doubles employee productivity, that’s an easy sell. For a modest increase, it is an easy ‘no’.

And LLMs become obsolete fast. A coding assistant that doesn’t know about new language features and which always uses deprecated or removed APIs is a productivity drain, not a help. A search assistant that has no knowledge of current events past 2025 will rapidly become useless. For translation, this is slower, but language evolves, both in terms of new words and new meanings for words, so these need retraining. For some use cases, RAG helps patch over the limitations of a stale model, but it also drives up the inference costs.

And all of that is assuming that the large-scale plagiarism for training these models is legal. At the moment governments are believing the ‘this will add massive amounts to your GDP’ hype from big tech and so are willing to throw creative industries under a bus in aid of better AI models. When they realise that the hype is built on lies, this will change.